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import torch.nn as nn
import numpy as np
from abc import abstractmethod
from tensorboardX import SummaryWriter
print('Ori meta template.')
class MetaTemplate(nn.Module):
def __init__(self, model_func, n_way, n_support, flatten=True, leakyrelu=False, tf_path=None, change_way=True):
super(MetaTemplate, self).__init__()
self.n_way = n_way
self.n_support = n_support
self.n_query = -1 #(change depends on input)
self.feature = model_func(flatten=flatten, leakyrelu=leakyrelu)
self.feat_dim = self.feature.final_feat_dim
self.change_way = change_way #some methods allow different_way classification during training and test
self.tf_writer = SummaryWriter(log_dir=tf_path) if tf_path is not None else None
@abstractmethod
def set_forward(self,x,is_feature):
pass
@abstractmethod
def set_forward_loss(self, x):
pass
def forward(self,x):
out = self.feature.forward(x)
return out
def parse_feature(self,x,is_feature):
x = x.cuda()
if is_feature:
z_all = x
else:
x = x.contiguous().view( self.n_way * (self.n_support + self.n_query), *x.size()[2:])
z_all = self.feature.forward(x)
z_all = z_all.view( self.n_way, self.n_support + self.n_query, -1)
z_support = z_all[:, :self.n_support]
z_query = z_all[:, self.n_support:]
return z_support, z_query
def correct(self, x):
scores, loss = self.set_forward_loss(x)
y_query = np.repeat(range( self.n_way ), self.n_query )
topk_scores, topk_labels = scores.data.topk(1, 1, True, True)
topk_ind = topk_labels.cpu().numpy()
top1_correct = np.sum(topk_ind[:,0] == y_query)
return float(top1_correct), len(y_query), loss.item()*len(y_query)
def train_loop(self, epoch, train_loader, optimizer, total_it):
print_freq = len(train_loader) // 10
avg_loss=0
for i, (x,_ ) in enumerate(train_loader):
self.n_query = x.size(1) - self.n_support
if self.change_way:
self.n_way = x.size(0)
optimizer.zero_grad()
_, loss = self.set_forward_loss(x)
loss.backward()
optimizer.step()
avg_loss = avg_loss+loss.item()
if (i + 1) % print_freq==0:
print('Epoch {:d} | Batch {:d}/{:d} | Loss {:f}'.format(epoch, i + 1, len(train_loader), avg_loss/float(i+1)))
if (total_it + 1) % 10 == 0 and self.tf_writer is not None:
self.tf_writer.add_scalar(self.method + '/query_loss', loss.item(), total_it + 1)
total_it += 1
return total_it
def test_loop(self, test_loader, record = None):
loss = 0.
count = 0
acc_all = []
iter_num = len(test_loader)
for i, (x,_) in enumerate(test_loader):
self.n_query = x.size(1) - self.n_support
if self.change_way:
self.n_way = x.size(0)
correct_this, count_this, loss_this = self.correct(x)
acc_all.append(correct_this/ count_this*100 )
loss += loss_this
count += count_this
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('--- %d Loss = %.6f ---' %(iter_num, loss/count))
print('--- %d Test Acc = %4.2f%% +- %4.2f%% ---' %(iter_num, acc_mean, 1.96* acc_std/np.sqrt(iter_num)))
return acc_mean
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